Federated self-supervised learning (FSSL) enables collaborative training of self-supervised representation models without sharing raw unlabeled data. While it serves as a crucial paradigm for privacy-preserving learning, its security remains vulnerable to backdoor attacks, where malicious clients manipulate local training to inject targeted backdoors. Existing FSSL attack methods, however, often suffer from low utilization of poisoned samples, limited transferability, and weak persistence. To address these limitations, we propose a new backdoor attack method for FSSL, namely Hallucinated Positive Entanglement (HPE). HPE first employs hallucination-based augmentation using synthetic positive samples to enhance the encoder's embedding of backdoor features. It then introduces feature entanglement to enforce tight binding between triggers and backdoor samples in the representation space. Finally, selective parameter poisoning and proximity-aware updates constrain the poisoned model within the vicinity of the global model, enhancing its stability and persistence. Experimental results on several FSSL scenarios and datasets show that HPE significantly outperforms existing backdoor attack methods in performance and exhibits strong robustness under various defense mechanisms.

翻译：联邦自监督学习（FSSL）使得无需共享原始未标注数据即可协作训练自监督表征模型。尽管其作为隐私保护学习的重要范式，但其安全性仍易受后门攻击威胁——恶意客户端可通过操纵本地训练过程注入定向后门。然而，现有FSSL攻击方法常存在毒化样本利用率低、可迁移性有限及持久性弱等缺陷。为克服这些局限，本文提出一种面向FSSL的新型后门攻击方法——幻觉正样本纠缠（HPE）。HPE首先采用基于合成正样本的幻觉增强技术，以提升编码器对后门特征的嵌入能力；继而引入特征纠缠机制，在表征空间中强制触发器与后门样本形成紧密绑定；最后通过选择性参数毒化与邻近感知更新，将毒化模型约束在全局模型邻域内，从而增强其稳定性与持久性。在多种FSSL场景与数据集上的实验结果表明，HPE在攻击性能上显著优于现有后门攻击方法，并在多种防御机制下展现出强鲁棒性。